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lllllllama/RigorPilot-Skills
README-first research reproduction skills with bounded execution, auditable evidence, and byte-preserving README annotations.
History
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Verified commit fb3ccdf5aa64b1fa5b1d0463e666a8bc8bc26e01
git clone --filter=blob:none --no-checkout https://github.com/lllllllama/RigorPilot-Skills.git .hotskills/lllllllama-rigorpilot-skills && git -C .hotskills/lllllllama-rigorpilot-skills checkout --detach fb3ccdf5aa64b1fa5b1d0463e666a8bc8bc26e01
# Copy each selected skill directory from .hotskills/lllllllama-rigorpilot-skills into .claude/skills/<skill-name>/git clone --filter=blob:none --no-checkout https://github.com/lllllllama/RigorPilot-Skills.git .hotskills/lllllllama-rigorpilot-skills && git -C .hotskills/lllllllama-rigorpilot-skills checkout --detach fb3ccdf5aa64b1fa5b1d0463e666a8bc8bc26e01
# Copy each selected skill directory from .hotskills/lllllllama-rigorpilot-skills into .agents/skills/<skill-name>/Heat
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Individual skills in this repository
- ai-research-explore
skills/ai-research-explore - ai-research-reproduction
skills/ai-research-reproduction - analyze-project
skills/analyze-project - env-and-assets-bootstrap
skills/env-and-assets-bootstrap - explore-code
skills/explore-code - explore-run
skills/explore-run - minimal-run-and-audit
skills/minimal-run-and-audit - paper-context-resolver
skills/paper-context-resolver - repo-intake-and-plan
skills/repo-intake-and-plan - run-train
skills/run-train - safe-debug
skills/safe-debug
How it works
ai-research-explore
When your agent uses it
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experimentation.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
1,430 tokensWhen the skill is activated
Reference documents
7,795 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Fit
- Research Rhythm
- Workflow
- Ranking and Evidence
- Campaign Inputs
- Reference Loading
Scripts executed separately
scripts/lookup/__init__.pyscripts/lookup/cache_store.pyscripts/lookup/inventory_writer.pyscripts/lookup/normalizers.pyscripts/lookup/providers/__init__.pyscripts/lookup/providers/arxiv_provider.pyscripts/lookup/providers/base.pyscripts/lookup/providers/doi_provider.pyscripts/lookup/providers/github_provider.pyscripts/lookup/providers/optional_provider.pyscripts/lookup/providers/url_provider.pyscripts/lookup/record_schema.pyscripts/lookup/repo_extractors.pyscripts/lookup/source_support.pyscripts/orchestrate_explore.pyscripts/passes/__init__.pyscripts/passes/atomic_idea_decomposition.pyscripts/passes/candidate_idea_generation.pyscripts/passes/execution_feasibility.pyscripts/passes/idea_cards.pyscripts/passes/idea_ranking.pyscripts/passes/implementation_fidelity.pyscripts/passes/improvement_bank.pyscripts/passes/lookup_sources.pyscripts/passes/source_mapping.pyscripts/write_outputs.py
Files
SKILL.mdcore6,701 bytesSKILL.mdcore6,701 bytesSKILL.mdcore6,701 bytesSKILL.mdcore6,701 bytesagents/openai.yamlasset423 bytesreferences/ai-research-explore-policy.mddoc1,803 bytesreferences/idea-evaluation-framework.mddoc1,117 bytesreferences/research-campaign-spec.mddoc9,663 bytesreferences/smoke-validation-policy.mddoc787 bytesreferences/source-mapping-policy.mddoc615 bytesreferences/sources-naming-policy.mddoc895 bytesscripts/lookup/__init__.pyruns624 bytesscripts/lookup/cache_store.pyruns8,607 bytesscripts/lookup/inventory_writer.pyruns3,139 bytesscripts/lookup/normalizers.pyruns4,907 bytesscripts/lookup/providers/__init__.pyruns457 bytesscripts/lookup/providers/arxiv_provider.pyruns2,864 bytesscripts/lookup/providers/base.pyruns3,086 bytesscripts/lookup/providers/doi_provider.pyruns2,828 bytesscripts/lookup/providers/github_provider.pyruns3,382 bytesscripts/lookup/providers/optional_provider.pyruns844 bytesscripts/lookup/providers/url_provider.pyruns2,090 bytesscripts/lookup/record_schema.pyruns3,445 bytesscripts/lookup/repo_extractors.pyruns3,064 bytesscripts/lookup/source_support.pyruns4,505 bytesscripts/orchestrate_explore.pyruns124,846 bytesscripts/passes/__init__.pyruns909 bytesscripts/passes/atomic_idea_decomposition.pyruns12,815 bytesscripts/passes/candidate_idea_generation.pyruns22,068 bytesscripts/passes/execution_feasibility.pyruns19,961 bytesscripts/passes/idea_cards.pyruns1,788 bytesscripts/passes/idea_ranking.pyruns8,649 bytesscripts/passes/implementation_fidelity.pyruns16,637 bytesscripts/passes/improvement_bank.pyruns19,244 bytesscripts/passes/lookup_sources.pyruns16,634 bytesscripts/passes/source_mapping.pyruns19,584 bytesscripts/write_outputs.pyruns1,194 bytes../ai-research-reproduction/references/agent-operating-principles.mddoc2,768 bytes../ai-research-reproduction/references/continuous-learning-policy.mddoc3,825 bytes../ai-research-reproduction/references/deep-learning-experiment-principles.mddoc2,077 bytes../ai-research-reproduction/references/explore-variant-spec.mddoc4,215 bytes../ai-research-reproduction/references/research-rigor-principles.mddoc3,754 bytes../ai-research-reproduction/references/research-thinking-loop.mddoc4,189 bytes
Read SKILL.md
# ai-research-explore
## Purpose
Use this as the Rigor Explore compatible skill slug after the researcher
explicitly authorizes candidate-only work on top of a durable
`current_research` anchor. The installed slug remains `ai-research-explore` for
compatibility. Rigor Explore is for meaningful and potentially novel deep
learning research candidates while preserving scientific rigor, comparability,
reproducibility, and auditable collaboration. Novelty and significance remain
hypotheses before literature contrast, ablation evidence, and fair comparison.
The skill does not promise autonomous discovery, global benchmark completeness,
novelty proof, or trusted reproduction success.
View the complete source on GitHubai-research-reproduction
When your agent uses it
Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.
What it loads into context
Frontmatter
172 tokensAlways available to the agent
SKILL.md
1,436 tokensWhen the skill is activated
Reference documents
14,158 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Purpose
- Fast Path
- Fit
- Trusted Target Selection
- Workflow
- Patch Boundary
- Outputs
- Reference Loading
Scripts executed separately
_bundled/shared/scripts/agent_provider.py_bundled/shared/scripts/command_utils.py_bundled/shared/scripts/lessons_store.py_bundled/shared/scripts/model_adapter.py_bundled/shared/scripts/resource_monitor.py_bundled/shared/scripts/runtime_runner.py_bundled/shared/scripts/task_queue.py_bundled/shared/scripts/write_explore_bundle.py_bundled/shared/scripts/write_run_bundle.py_bundled/skills/analyze-project/scripts/analyze_project.py_bundled/skills/env-and-assets-bootstrap/scripts/plan_setup.py_bundled/skills/env-and-assets-bootstrap/scripts/prepare_assets.py_bundled/skills/minimal-run-and-audit/scripts/write_outputs.py_bundled/skills/repo-intake-and-plan/scripts/extract_commands.py_bundled/skills/repo-intake-and-plan/scripts/scan_repo.py_bundled/skills/run-train/scripts/run_training.py_bundled/skills/run-train/scripts/write_outputs.pyscripts/annotate_readme.pyscripts/doctor.pyscripts/orchestrate_repro.pyscripts/repro_job.pyscripts/run_agent.py
Files
SKILL.mdcore7,123 bytesSKILL.mdcore7,123 bytesSKILL.mdcore7,123 bytes_bundled/MANIFEST.jsonasset5,394 bytes_bundled/shared/scripts/agent_provider.pyruns6,289 bytes_bundled/shared/scripts/command_utils.pyruns3,085 bytes_bundled/shared/scripts/lessons_store.pyruns8,608 bytes_bundled/shared/scripts/model_adapter.pyruns6,487 bytes_bundled/shared/scripts/resource_monitor.pyruns8,164 bytes_bundled/shared/scripts/runtime_runner.pyruns29,154 bytes_bundled/shared/scripts/task_queue.pyruns30,927 bytes_bundled/shared/scripts/write_explore_bundle.pyruns33,409 bytes_bundled/shared/scripts/write_run_bundle.pyruns31,741 bytes_bundled/skills/analyze-project/scripts/analyze_project.pyruns25,957 bytes_bundled/skills/env-and-assets-bootstrap/scripts/plan_setup.pyruns4,795 bytes_bundled/skills/env-and-assets-bootstrap/scripts/prepare_assets.pyruns4,288 bytes_bundled/skills/minimal-run-and-audit/scripts/write_outputs.pyruns1,171 bytes_bundled/skills/repo-intake-and-plan/scripts/extract_commands.pyruns13,256 bytes_bundled/skills/repo-intake-and-plan/scripts/scan_repo.pyruns2,937 bytes_bundled/skills/run-train/scripts/run_training.pyruns19,876 bytes_bundled/skills/run-train/scripts/write_outputs.pyruns1,173 bytesagents/openai.yamlasset389 bytesassets/COMMANDS.template.mddoc348 bytesassets/LOG.template.mddoc669 bytesassets/PATCHES.template.mddoc525 bytesassets/SUMMARY.template.mddoc621 bytesassets/model_profile.template.jsonasset183 bytesassets/readme_annotations.template.jsonasset1,101 bytesassets/status.template.jsonasset1,170 bytesreferences/agent-job.mddoc5,670 bytesreferences/agent-operating-principles.mddoc2,768 bytesreferences/agent-runner.mddoc10,444 bytesreferences/architecture.mddoc1,322 bytesreferences/continuous-learning-policy.mddoc3,825 bytesreferences/deep-learning-experiment-principles.mddoc2,077 bytesreferences/explore-variant-spec.mddoc4,215 bytesreferences/language-policy.mddoc794 bytesreferences/output-spec.mddoc9,248 bytesreferences/patch-policy.mddoc1,678 bytesreferences/readme-annotation-policy.mddoc4,403 bytesreferences/research-pitfall-checklist.mddoc1,665 bytesreferences/research-rigor-principles.mddoc3,754 bytesreferences/research-safety-principles.mddoc1,925 bytesreferences/research-thinking-loop.mddoc4,189 bytesreferences/runtime-and-model-adapter.mddoc5,470 bytesscripts/annotate_readme.pyruns36,767 bytesscripts/doctor.pyruns3,621 bytesscripts/orchestrate_repro.pyruns118,106 bytesscripts/repro_job.pyruns19,806 bytesscripts/run_agent.pyruns39,314 bytes
Read SKILL.md
# ai-research-reproduction
## Purpose
Guide README-first deep learning reproduction toward the smallest trustworthy
run with auditable evidence. Preserve documented meaning; record assumptions,
deviations and blockers instead of changing semantics to manufacture success.
Load specialized references only for a concrete uncertainty.
## Fast Path
For a routine bounded run, keep the control path short:
1. Read the target README and only the target test/config/source needed to understand the documented command.View the complete source on GitHubanalyze-project
When your agent uses it
Rigor Analyze / Rigor Audit read-only skill for deep learning research repositories. Use when the user wants to read and understand a repository, inspect model structure and training or inference entrypoints, review configs and insertion points, or flag suspicious implementation patterns without modifying code or running heavy jobs. Do not use for active command execution, broad refactoring, speculative code adaptation, or automatic bug fixing.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
405 tokensWhen the skill is activated
Reference documents
937 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Output expectations
- Notes
Scripts executed separately
scripts/analyze_project.py
Files
../ai-research-reproduction/references/agent-operating-principles.mddoc2,768 bytes../ai-research-reproduction/references/research-pitfall-checklist.mddoc1,665 bytesSKILL.mdcore2,074 bytesagents/openai.yamlasset396 bytesreferences/analysis-policy.mddoc545 bytesscripts/analyze_project.pyruns25,957 bytes
Read SKILL.md
# analyze-project
Use this as the Rigor Analyze / Rigor Audit read-only skill. The installed slug
remains `analyze-project` for compatibility.
Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide
read-only analysis without constraining the model's project-specific reasoning.
## When to apply
- The user wants to understand a deep learning repository before changing it.
- The user needs a map of model structure, training entrypoints, inference entrypoints, and config relationships.
- The user wants conservative suggestions about likely insertion points or suspicious implementation patterns.View the complete source on GitHubenv-and-assets-bootstrap
When your agent uses it
Rigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
459 tokensWhen the skill is activated
Reference documents
918 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Input expectations
- Output expectations
- Notes
Scripts executed separately
scripts/bootstrap_env.pyscripts/bootstrap_env.shscripts/plan_setup.pyscripts/prepare_assets.py
Files
../ai-research-reproduction/references/agent-operating-principles.mddoc2,768 bytesSKILL.mdcore2,366 bytesSKILL.mdcore2,366 bytesagents/openai.yamlasset314 bytesreferences/assets-policy.mddoc826 bytesreferences/env-policy.mddoc1,171 bytesscripts/bootstrap_env.pyruns5,365 bytesscripts/bootstrap_env.shruns270 bytesscripts/plan_setup.pyruns4,795 bytesscripts/prepare_assets.pyruns4,288 bytes
Read SKILL.md
# env-and-assets-bootstrap
Use this as the Rigor Setup skill. The installed slug remains
`env-and-assets-bootstrap` for compatibility.
Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should keep setup
planning conservative while leaving environment-specific judgment to the model.
## When to apply
- After repo intake identifies a credible reproduction target.
- When environment creation or asset path preparation is needed before running commands.
- When the repo depends on checkpoints, datasets, or cache directories.View the complete source on GitHubexplore-code
When your agent uses it
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
525 tokensWhen the skill is activated
Reference documents
1,348 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Output expectations
- Notes
Scripts executed separately
scripts/plan_code_changes.pyscripts/write_outputs.py
Files
../ai-research-reproduction/references/agent-operating-principles.mddoc2,768 bytes../ai-research-reproduction/references/research-rigor-principles.mddoc3,754 bytesSKILL.mdcore2,592 bytesagents/openai.yamlasset326 bytesreferences/explore-policy.mddoc652 bytesscripts/plan_code_changes.pyruns14,526 bytesscripts/write_outputs.pyruns1,186 bytes
Read SKILL.md
# explore-code
Use this as the Rigor Improve implementation leaf skill. The installed slug
remains `explore-code` for compatibility.
Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide
bounded candidate code work without over-prescribing implementation details.
## When to apply
- When the researcher explicitly authorizes exploratory code changes on an isolated branch or worktree.
- When the task is source-anchored module transplant, backbone adaptation, LoRA or adapter insertion, or low-risk module combination.
- When summary-level recording is sufficient and the result is a candidate, not a trusted conclusion.View the complete source on GitHubexplore-run
When your agent uses it
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
686 tokensWhen the skill is activated
Reference documents
2,063 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Ranking Semantics
- Variant Spec Hints
- Output expectations
- Notes
Scripts executed separately
scripts/plan_variants.pyscripts/write_outputs.py
Files
../ai-research-reproduction/references/agent-operating-principles.mddoc2,768 bytes../ai-research-reproduction/references/deep-learning-experiment-principles.mddoc2,077 bytes../ai-research-reproduction/references/explore-variant-spec.mddoc4,215 bytesSKILL.mdcore3,332 bytesagents/openai.yamlasset334 bytesreferences/execution-policy.mddoc674 bytesscripts/plan_variants.pyruns10,708 bytesscripts/write_outputs.pyruns1,184 bytes
Read SKILL.md
# explore-run
Use this as the Rigor Improve / Rigor Explore run leaf skill. The installed slug
remains `explore-run` for compatibility.
Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide
candidate run planning while preserving model judgment about the active repo.
## When to apply
- When the researcher explicitly authorizes exploratory runs.
- When the task is a small-subset validation, short-cycle training probe, batch sweep, idle-GPU search, or quick transfer-learning trial.
- When the output should rank candidate runs rather than certify trusted success.View the complete source on GitHubminimal-run-and-audit
When your agent uses it
Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
547 tokensWhen the skill is activated
Reference documents
1,385 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Input expectations
- Output expectations
- Notes
Scripts executed separately
scripts/run_command.pyscripts/write_outputs.py
Files
../ai-research-reproduction/references/agent-operating-principles.mddoc2,768 bytes../ai-research-reproduction/references/research-rigor-principles.mddoc3,754 bytesSKILL.mdcore2,761 bytesagents/openai.yamlasset313 bytesreferences/reporting-policy.mddoc747 bytesscripts/run_command.pyruns13,638 bytesscripts/write_outputs.pyruns1,171 bytes
Read SKILL.md
# minimal-run-and-audit
Use this as the Rigor Run skill. The installed slug remains
`minimal-run-and-audit` for compatibility.
Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should make run
evidence auditable without turning every command into a rigid protocol.
## When to apply
- After a reproduction target and setup plan exist.
- When the main skill needs execution evidence and normalized outputs.
- When a smoke test, documented inference run, documented evaluation run, or other short non-training verification is appropriate.View the complete source on GitHubpaper-context-resolver
When your agent uses it
Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
384 tokensWhen the skill is activated
Reference documents
261 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Input expectations
- Output expectations
- Notes
Files
SKILL.mdcore2,066 bytesSKILL.mdcore2,066 bytesagents/openai.yamlasset368 bytesreferences/paper-assisted-reproduction.mddoc1,327 bytes
Read SKILL.md
# paper-context-resolver
Use this as the Rigor Paper Context helper. The installed slug remains
`paper-context-resolver` for compatibility.
## When to apply
- README and repo files leave a reproduction-critical gap.
- The gap concerns dataset version, split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumptions.
- The main skill needs a narrow evidence supplement instead of a full paper summary.
- There is already a concrete reproduction question to answer.
## When not to apply
View the complete source on GitHubrepo-intake-and-plan
When your agent uses it
Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
411 tokensWhen the skill is activated
Reference documents
261 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Input expectations
- Output expectations
- Notes
Scripts executed separately
scripts/extract_commands.pyscripts/scan_repo.py
Files
SKILL.mdcore2,123 bytesagents/openai.yamlasset368 bytesreferences/repo-scan-rules.mddoc1,114 bytesscripts/extract_commands.pyruns13,256 bytesscripts/scan_repo.pyruns2,937 bytes
Read SKILL.md
# repo-intake-and-plan
Use this as the Rigor Intake helper. The installed slug remains
`repo-intake-and-plan` for compatibility.
## When to apply
- At the beginning of README-first reproduction work.
- When the main skill needs a fast map of repo structure and documented commands.
- When inference, evaluation, and training candidates must be classified conservatively.
- When the user explicitly wants to inspect the repo first and not run anything yet.
## When not to apply
View the complete source on GitHubrun-train
When your agent uses it
Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
510 tokensWhen the skill is activated
Reference documents
1,119 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Input expectations
- Output expectations
- Notes
Scripts executed separately
scripts/run_training.pyscripts/write_outputs.py
Files
../ai-research-reproduction/references/agent-operating-principles.mddoc2,768 bytes../ai-research-reproduction/references/deep-learning-experiment-principles.mddoc2,077 bytesSKILL.mdcore2,529 bytesagents/openai.yamlasset331 bytesreferences/training-policy.mddoc1,097 bytesscripts/run_training.pyruns19,876 bytesscripts/write_outputs.pyruns1,173 bytes
Read SKILL.md
# run-train
Use this as the Rigor Train skill. The installed slug remains `run-train` for
compatibility.
Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should keep
training evidence bounded while leaving repository-specific monitoring details
to the model.
## When to apply
- When the training command has already been selected and should be executed conservatively.
- When the researcher wants startup verification, short-run verification, full training kickoff, or resume handling.View the complete source on GitHubsafe-debug
When your agent uses it
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
429 tokensWhen the skill is activated
Reference documents
1,634 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to apply
- When not to apply
- Clear boundaries
- Output expectations
- Notes
Scripts executed separately
scripts/safe_debug.py
Files
../ai-research-reproduction/references/agent-operating-principles.mddoc2,768 bytes../ai-research-reproduction/references/research-pitfall-checklist.mddoc1,665 bytes../ai-research-reproduction/references/research-rigor-principles.mddoc3,754 bytesSKILL.mdcore2,086 bytesagents/openai.yamlasset366 bytesreferences/debug-policy.mddoc511 bytesscripts/safe_debug.pyruns6,688 bytes
Read SKILL.md
# safe-debug
Use this as the Rigor Debug / Rigor Audit skill. The installed slug remains
`safe-debug` for compatibility.
Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide
conservative diagnosis without blocking the model from finding the local root
cause.
## When to apply
- The user provides a traceback, terminal error, or concrete training or inference failure symptom.
- The user wants diagnosis, root-cause narrowing, and minimal patch suggestions before code is changed.View the complete source on GitHubSkills included
- ai-research-explore
- ai-research-reproduction
- analyze-project
- env-and-assets-bootstrap
- explore-code
- explore-run
- minimal-run-and-audit
- paper-context-resolver
- repo-intake-and-plan
- run-train
- safe-debug
Repository facts
- License
- MIT
- Created
- Mar 30, 2026
- Last pushed
- Sep 23, 2026
- Forks
- 19
- Open issues
- 1
- Project activity
- Active
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 5, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 7 notes
skills/ai-research-explore/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/ai-research-explore/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/ai-research-explore/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/ai-research-reproduction/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/ai-research-reproduction/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/env-and-assets-bootstrap/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/paper-context-resolver/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- 73 network referencesin 12 files: ai-research-reproduction/_bundled/shared/scripts/agent_provider.py:4 ai-research-explore/scripts/lookup/normalizers.py:41 ai-research-explore/scripts/lookup/providers/arxiv_provider.py:34 and 9 more
- 1 obfuscation signalin 1 file: ai-research-explore/scripts/lookup/providers/github_provider.py:30
- No instruction overrides
- 64 executable scriptsin 64 files: ai-research-explore/scripts/lookup/__init__.py:1 ai-research-explore/scripts/lookup/cache_store.py:1 ai-research-explore/scripts/lookup/inventory_writer.py:1 and 61 more
- 20 references to secretsin 11 files: ai-research-explore/scripts/lookup/providers/optional_provider.py:10 ai-research-reproduction/scripts/run_agent.py:65 ai-research-reproduction/_bundled/shared/scripts/lessons_store.py:42 and 8 more
- No requests to vote in Hot
Show all 158 signals by file
skills/ai-research-explore/scripts/lookup/__init__.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/lookup/cache_store.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/lookup/inventory_writer.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/lookup/normalizers.py
- Executable scriptline 1
Executable file - Network accessline 41
return text if text.lower().startswith("doi:"): return f"https://doi.org/{text[4:].strip()}" - Network accessline 42
if text.lower().startswith("doi:"): return f"https://doi.org/{text[4:].strip()}" return text - Network accessline 43
return f"https://doi.org/{text[4:].strip()}" - Network accessline 86
"identifier": arxiv_id, "arxiv_id": arxiv_id, "url": f"https://arxiv.org/abs/{arxiv_id}", - Network accessline 87
"arxiv_id": arxiv_id, "url": f"https://arxiv.org/abs/{arxiv_id}", } - Network accessline 88
"url": f"https://arxiv.org/abs/{arxiv_id}", - Network accessline 105
"identifier": doi, "doi": doi, "url": f"https://doi.org/{doi}", - Network accessline 106
"doi": doi, "url": f"https://doi.org/{doi}", } - Network accessline 107
"url": f"https://doi.org/{doi}", - Network accessline 133
"repo": repo, "source_file": source_file, "url": f"https://github.com/{owner}/{repo}", - Network accessline 134
"source_file": source_file, "url": f"https://github.com/{owner}/{repo}", } - Network accessline 135
"url": f"https://github.com/{owner}/{repo}",
skills/ai-research-explore/scripts/lookup/providers/__init__.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/lookup/providers/arxiv_provider.py
- Executable scriptline 1
Executable file - Network accessline 34
try: payload = http_get( f"https://export.arxiv.org/api/query?id_list={urllib.parse.quote(arxiv_id)}", - Network accessline 35
payload = http_get( f"https://export.arxiv.org/api/query?id_list={urllib.parse.quote(arxiv_id)}", accept="application/atom+xml, text/xml;q=0.9", - Network accessline 36
f"https://export.arxiv.org/api/query?id_list={urllib.parse.quote(arxiv_id)}", - Network accessline 38
) root = ET.fromstring(payload.decode("utf-8", errors="ignore")) namespace = {"atom": "http://www.w3.org/2005/Atom"} - Network accessline 39
root = ET.fromstring(payload.decode("utf-8", errors="ignore")) namespace = {"atom": "http://www.w3.org/2005/Atom"} entry = root.find("atom:entry", namespace) - Network accessline 40
namespace = {"atom": "http://www.w3.org/2005/Atom"}
skills/ai-research-explore/scripts/lookup/providers/base.py
- Executable scriptline 1
Executable file - Network accessline 60
def http_get(url: str, *, accept: str = "application/json, text/plain;q=0.9, text/html;q=0.8") -> bytes: request = urllib.request.Request( - Network accessline 61
request = urllib.request.Request( - Network accessline 66
}, ) with urllib.request.urlopen(request, timeout=REQUEST_TIMEOUT_SECONDS) as response: - Network accessline 67
) with urllib.request.urlopen(request, timeout=REQUEST_TIMEOUT_SECONDS) as response: return response.read() - Network accessline 68
with urllib.request.urlopen(request, timeout=REQUEST_TIMEOUT_SECONDS) as response:
skills/ai-research-explore/scripts/lookup/providers/doi_provider.py
- Executable scriptline 1
Executable file - Network accessline 19
"normalized_id": locator_info.get("normalized_id", f"doi:{doi}"), "title": f"DOI:{doi}" if doi else "", "url": locator_info.get("url", f"https://doi.org/{doi}" if doi else ""), - Network accessline 20
"title": f"DOI:{doi}" if doi else "", "url": locator_info.get("url", f"https://doi.org/{doi}" if doi else ""), "authors": [], - Network accessline 21
"url": locator_info.get("url", f"https://doi.org/{doi}" if doi else ""), - Network accessline 34
try: payload = http_get( f"https://doi.org/{urllib.parse.quote(doi, safe='/')}", - Network accessline 35
payload = http_get( f"https://doi.org/{urllib.parse.quote(doi, safe='/')}", accept="application/vnd.citationstyles.csl+json, application/json;q=0.9", - Network accessline 36
f"https://doi.org/{urllib.parse.quote(doi, safe='/')}",
skills/ai-research-explore/scripts/lookup/providers/github_provider.py
- Executable scriptline 1
Executable file - Network accessline 21
def _fetch_readme(owner: str, repo: str) -> Dict[str, Any]: try: payload = http_get_json(f"https://api.github.com/repos/{owner}/{repo}/readme") - Network accessline 22
try: payload = http_get_json(f"https://api.github.com/repos/{owner}/{repo}/readme") except Exception: - Network accessline 23
payload = http_get_json(f"https://api.github.com/repos/{owner}/{repo}/readme") - Possible obfuscationline 30
decoded = base64.b64decode(str(content).encode("utf-8"), validate=False).decode("utf-8", errors="ignore") - Network accessline 65
return record try: payload = http_get_json(f"https://api.github.com/repos/{owner}/{repo}") - Network accessline 66
try: payload = http_get_json(f"https://api.github.com/repos/{owner}/{repo}") readme_meta = _fetch_readme(owner, repo) - Network accessline 67
payload = http_get_json(f"https://api.github.com/repos/{owner}/{repo}")
skills/ai-research-explore/scripts/lookup/providers/optional_provider.py
skills/ai-research-explore/scripts/lookup/providers/url_provider.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/lookup/record_schema.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/lookup/repo_extractors.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/lookup/source_support.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/orchestrate_explore.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-explore/scripts/passes/__init__.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/atomic_idea_decomposition.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/candidate_idea_generation.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/execution_feasibility.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/idea_cards.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/idea_ranking.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/implementation_fidelity.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/improvement_bank.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/lookup_sources.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/passes/source_mapping.py
- Executable scriptline 1
Executable file
skills/ai-research-explore/scripts/write_outputs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/shared/scripts/agent_provider.py
- Executable scriptline 1
Executable file - Network accessline 4
import json import http.client - Network accessline 5
import http.client - Network accessline 33
elif isinstance(error, TimeoutError): code = "timeout" elif isinstance(error, http.client.HTTPException): - Network accessline 34
code = "timeout" elif isinstance(error, http.client.HTTPException): code = "invalid_response" - Network accessline 35
elif isinstance(error, http.client.HTTPException): - Network accessline 75
def complete(self, messages: list, system: str, tools: list, max_tokens: int, timeout: float) -> dict: profile = self.profile endpoint = str(profile.get("endpoint") or os.getenv("ANTHROPIC_BASE_URL") or "https://api.anthropi - Network accessline 76
profile = self.profile endpoint = str(profile.get("endpoint") or os.getenv("ANTHROPIC_BASE_URL") or "https://api.anthropic.com").rstrip("/") parsed = urlsplit(endpoint) - Network accessline 77
endpoint = str(profile.get("endpoint") or os.getenv("ANTHROPIC_BASE_URL") or "https://api.anthropic.com").rstrip("/") - Access to secretsline 83
[Credential-related source omitted] - Network accessline 92
payload = {"model": profile["model"], "system": system, "messages": messages, "tools": tools, "max_tokens": max_tokens, **self.parameters} request = urllib.request.Request(url, data=json.dumps(payload).encode(), h - Network accessline 93
"tools": tools, "max_tokens": max_tokens, **self.parameters} request = urllib.request.Request(url, data=json.dumps(payload).encode(), headers=headers) try: - Network accessline 94
request = urllib.request.Request(url, data=json.dumps(payload).encode(), headers=headers) - Network accessline 102
except urllib.error.URLError as exc: raise transport_error(exc.reason) from None except (OSError, http.client.HTTPException) as exc: - Network accessline 103
raise transport_error(exc.reason) from None except (OSError, http.client.HTTPException) as exc: raise transport_error(exc) from None - Network accessline 104
except (OSError, http.client.HTTPException) as exc:
skills/ai-research-reproduction/_bundled/shared/scripts/command_utils.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/shared/scripts/lessons_store.py
skills/ai-research-reproduction/_bundled/shared/scripts/model_adapter.py
skills/ai-research-reproduction/_bundled/shared/scripts/resource_monitor.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/shared/scripts/runtime_runner.py
skills/ai-research-reproduction/_bundled/shared/scripts/task_queue.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/shared/scripts/write_explore_bundle.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/shared/scripts/write_run_bundle.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/skills/analyze-project/scripts/analyze_project.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/skills/env-and-assets-bootstrap/scripts/plan_setup.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/skills/env-and-assets-bootstrap/scripts/prepare_assets.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/skills/minimal-run-and-audit/scripts/write_outputs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/skills/repo-intake-and-plan/scripts/extract_commands.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 126
"cd ", ) ASSET_PREFIXES = ("wget ", "curl ", "mkdir ", "tar ", "unzip ", "7z ", "aria2c ") - Network accessline 128
ASSET_PREFIXES = ("wget ", "curl ", "mkdir ", "tar ", "unzip ", "7z ", "aria2c ") - Network accessline 189
if re.search(r"[\\/].+\.(?:py|sh|bat)", candidate): return True if candidate.startswith(("cd ", "ls ", "mkdir ", "wget ", "curl ", "git ")): - Network accessline 190
return True if candidate.startswith(("cd ", "ls ", "mkdir ", "wget ", "curl ", "git ")): return True - Network accessline 191
if candidate.startswith(("cd ", "ls ", "mkdir ", "wget ", "curl ", "git ")):
skills/ai-research-reproduction/_bundled/skills/repo-intake-and-plan/scripts/scan_repo.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/skills/run-train/scripts/run_training.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/_bundled/skills/run-train/scripts/write_outputs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/assets/model_profile.template.json
- Access to secretsline 5
[Credential-related source omitted]
skills/ai-research-reproduction/references/agent-runner.md
- Network accessline 4
The existing deterministic orchestrator remains available. No SDK installation is needed: the client uses Python's standard library. Protocol reference: https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview - Network accessline 6
https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview - Access to secretsline 98
[Credential-related source omitted] - Access to secretsline 105
[Credential-related source omitted] - Network accessline 122
fields are rejected before HTTP; `max_tokens` remains controlled by the task budget. Leave sampling settings absent unless the selected model supports them: the [Messages API](https://platform.claude.com/docs/en/api/messages/create) - Network accessline 123
budget. Leave sampling settings absent unless the selected model supports them: the [Messages API](https://platform.claude.com/docs/en/api/messages/create) deprecates these controls for newer models. A local protocol test is not a - Network accessline 124
the [Messages API](https://platform.claude.com/docs/en/api/messages/create)
skills/ai-research-reproduction/references/runtime-and-model-adapter.md
- Network accessline 63
"revision": "2026-09-01", "capabilities": ["text", "tool_calling", "structured_output"], "endpoint": "https://gateway.example/v1", - Network accessline 64
"capabilities": ["text", "tool_calling", "structured_output"], "endpoint": "https://gateway.example/v1", "credential_env": "LAB_MODEL_API_KEY", - Network accessline 65
"endpoint": "https://gateway.example/v1", - Access to secretsline 66
[Credential-related source omitted]
skills/ai-research-reproduction/scripts/annotate_readme.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/scripts/doctor.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/ai-research-reproduction/scripts/orchestrate_repro.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 105
[Credential-related source omitted] - Network accessline 931
"pip install ", "pip3 install ", "python -m pip install ", "python3 -m pip install ", "uv pip install ", "poetry install ", "conda install ", "conda create ", "conda env create ", "conda activate ", "git clone ", "wget ", "curl ", " - Network accessline 932
"uv pip install ", "poetry install ", "conda install ", "conda create ", "conda env create ", "conda activate ", "git clone ", "wget ", "curl ", "aria2c ", "make install ", "npm install ", "npm ci ", "yarn install ", "pnpm install " - Network accessline 933
"conda activate ", "git clone ", "wget ", "curl ", "aria2c ", "make install ",
skills/ai-research-reproduction/scripts/repro_job.py
skills/ai-research-reproduction/scripts/run_agent.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 65
[Credential-related source omitted] - Access to secretsline 66
[Credential-related source omitted] - Access to secretsline 77
[Credential-related source omitted] - Network accessline 341
if any(not isinstance(v, int) or isinstance(v, bool) or v <= 0 for v in budget.values()): raise ValueError("All budget limits must be positive integers") endpoint_identity = fingerprint(profile.get("endpoint") or os.getenv("ANTH - Network accessline 342
raise ValueError("All budget limits must be positive integers") endpoint_identity = fingerprint(profile.get("endpoint") or os.getenv("ANTHROPIC_BASE_URL") or "https://api.anthropic.com") harness_identity = fingerprint([Path(__file__ - Network accessline 343
endpoint_identity = fingerprint(profile.get("endpoint") or os.getenv("ANTHROPIC_BASE_URL") or "https://api.anthropic.com") - Access to secretsline 469
[Credential-related source omitted]
skills/analyze-project/scripts/analyze_project.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/env-and-assets-bootstrap/scripts/bootstrap_env.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/env-and-assets-bootstrap/scripts/bootstrap_env.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/env-and-assets-bootstrap/scripts/plan_setup.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/env-and-assets-bootstrap/scripts/prepare_assets.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/explore-code/scripts/plan_code_changes.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/explore-code/scripts/write_outputs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/explore-run/scripts/plan_variants.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/explore-run/scripts/write_outputs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/minimal-run-and-audit/scripts/run_command.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/minimal-run-and-audit/scripts/write_outputs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/repo-intake-and-plan/scripts/extract_commands.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 126
"cd ", ) ASSET_PREFIXES = ("wget ", "curl ", "mkdir ", "tar ", "unzip ", "7z ", "aria2c ") - Network accessline 128
ASSET_PREFIXES = ("wget ", "curl ", "mkdir ", "tar ", "unzip ", "7z ", "aria2c ") - Network accessline 189
if re.search(r"[\\/].+\.(?:py|sh|bat)", candidate): return True if candidate.startswith(("cd ", "ls ", "mkdir ", "wget ", "curl ", "git ")): - Network accessline 190
return True if candidate.startswith(("cd ", "ls ", "mkdir ", "wget ", "curl ", "git ")): return True - Network accessline 191
if candidate.startswith(("cd ", "ls ", "mkdir ", "wget ", "curl ", "git ")):
skills/repo-intake-and-plan/scripts/scan_repo.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/run-train/scripts/run_training.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/run-train/scripts/write_outputs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/safe-debug/scripts/safe_debug.py
- Executable scriptline 1
#!/usr/bin/env python3
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Declared and executed: 9 facts
- Contacts doi.org, which SKILL.md, the README and the plugin manifest do not mentionai-research-explore/scripts/lookup/normalizers.py:43High confidence
- Contacts arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionai-research-explore/scripts/lookup/normalizers.py:88High confidence
- Contacts github.com, which SKILL.md, the README and the plugin manifest do not mentionai-research-explore/scripts/lookup/normalizers.py:135High confidence
- Contacts export.arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionai-research-explore/scripts/lookup/providers/arxiv_provider.py:36High confidence
- Contacts www.w3.org, which SKILL.md, the README and the plugin manifest do not mentionai-research-explore/scripts/lookup/providers/arxiv_provider.py:40High confidence
- Contacts doi.org, which SKILL.md, the README and the plugin manifest do not mentionai-research-explore/scripts/lookup/providers/doi_provider.py:21High confidence
- Contacts api.github.com, which SKILL.md, the README and the plugin manifest do not mentionai-research-explore/scripts/lookup/providers/github_provider.py:23High confidence
- Contacts api.anthropic.com, which SKILL.md, the README and the plugin manifest do not mentionai-research-reproduction/_bundled/shared/scripts/agent_provider.py:77High confidence
- Contacts api.anthropic.com, which SKILL.md, the README and the plugin manifest do not mentionai-research-reproduction/scripts/run_agent.py:343High confidence
Signal confidence: 157 facts
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/__init__.py:1Low confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/cache_store.py:1Low confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/inventory_writer.py:1Low confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/normalizers.py:1Low confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:41High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:42High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:43High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:86High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:87High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:88High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:105High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:106High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:107High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:133High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:134High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/normalizers.py:135High confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/providers/__init__.py:1Low confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/providers/arxiv_provider.py:1Low confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/arxiv_provider.py:34High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/arxiv_provider.py:35High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/arxiv_provider.py:36High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/arxiv_provider.py:38High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/arxiv_provider.py:39High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/arxiv_provider.py:40High confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/providers/base.py:1Low confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/base.py:60High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/base.py:61High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/base.py:66High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/base.py:67High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/base.py:68High confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/providers/doi_provider.py:1Low confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/doi_provider.py:19High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/doi_provider.py:20High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/doi_provider.py:21High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/doi_provider.py:34High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/doi_provider.py:35High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/doi_provider.py:36High confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/providers/github_provider.py:1Low confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/github_provider.py:21High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/github_provider.py:22High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/github_provider.py:23High confidence
- Possible obfuscation in code of a file that runsai-research-explore/scripts/lookup/providers/github_provider.py:30High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/github_provider.py:65High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/github_provider.py:66High confidence
- Network access in code of a file that runsai-research-explore/scripts/lookup/providers/github_provider.py:67High confidence
- Executable script in prose of a file that runsai-research-explore/scripts/lookup/providers/optional_provider.py:1Low confidence
- Access to secrets in code of a file that runsai-research-explore/scripts/lookup/providers/optional_provider.py:10High confidence
- Access to secrets in code of a file that runsai-research-explore/scripts/lookup/providers/optional_provider.py:11High confidence
- Access to secrets in code of a file that runsai-research-explore/scripts/lookup/providers/optional_provider.py:12High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valueai-research-explore/scripts/lookup/providers/optional_provider.py:21Low confidence
and 107 more
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Path .claude/commands/ai-research-explore.md | .claude/skills/<skill-name>/ |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/ai-research-explore/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/ai-research-explore/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/ai-research-explore/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/ai-research-explore/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.